Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.

tissue_of_interest = "Tongue"
library(here)
here() starts at /Users/olgabot/code/tabula-muris
source(here("00_data_ingest", "02_tissue_analysis_rmd", "boilerplate.R"))
package ‘dplyr’ was built under R version 3.4.2
Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union
tiss = load_tissue_droplet(tissue_of_interest)
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
[1] "Scaling data matrix"

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Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|

Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.

PCElbowPlot(object = tiss)

Choose the number of principal components to use.

# Set number of principal components. 
n.pcs = 14

The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale…higher resolution will give more clusters, lower resolution will give fewer.

For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.

# Set resolution 
res.used <- 0.4
tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)

To visualize

# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 20, perplexity=35, dim.embed = 2)
TSNEPlot(object = tiss, do.label = T, pt.size = 1.2, label.size = 4)

Check expression of genes of interset.

Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.

How big are the clusters?

table(tiss@ident)

   0    1    2    3    4    5    6    7    8    9   10 
1389 1095 1079  869  815  602  601  485  366  199   38 
tiss1=BuildClusterTree(tiss)
[1] "Finished averaging RNA for cluster 0"
[1] "Finished averaging RNA for cluster 1"
[1] "Finished averaging RNA for cluster 2"
[1] "Finished averaging RNA for cluster 3"
[1] "Finished averaging RNA for cluster 4"
[1] "Finished averaging RNA for cluster 5"
[1] "Finished averaging RNA for cluster 6"
[1] "Finished averaging RNA for cluster 7"
[1] "Finished averaging RNA for cluster 8"
[1] "Finished averaging RNA for cluster 9"
[1] "Finished averaging RNA for cluster 10"

#cluster1.markers <- FindMarkers(object = tiss, ident.1 = 1, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster2.markers <- FindMarkers(object = tiss, ident.1 = 2, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster3.markers <- FindMarkers(object = tiss, ident.1 = 3, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster4.markers <- FindMarkers(object = tiss, ident.1 = 4, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster5.markers <- FindMarkers(object = tiss, ident.1 = 5, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
cluster6.markers <- FindMarkers(object = tiss, ident.1 = 6, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~01m 26s      
   |++                                                | 2 % ~02m 11s      
   |++                                                | 3 % ~01m 55s      
   |+++                                               | 4 % ~01m 48s      
   |+++                                               | 5 % ~01m 43s      
   |++++                                              | 7 % ~01m 38s      
   |++++                                              | 8 % ~01m 33s      
   |+++++                                             | 9 % ~01m 29s      
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   |+++++++                                           | 13% ~01m 19s      
   |++++++++                                          | 14% ~01m 17s      
   |++++++++                                          | 15% ~01m 15s      
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   |++++++++++++++                                    | 27% ~60s          
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   |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~07s          
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   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 15s
#cluster7.markers <- FindMarkers(object = tiss, ident.1 = 7, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster8.markers <- FindMarkers(object = tiss, ident.1 = 8, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster9.markers <- FindMarkers(object = tiss, ident.1 = 9, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster0.markers <- FindMarkers(object = tiss, ident.1 = 0, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)

Which markers identify a specific cluster?

print(x = head(x = cluster6.markers, n = 30))

You can also compute all markers for all clusters at once. This may take some time.

tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~40s          
   |++                                                | 2 % ~38s          
   |++                                                | 3 % ~37s          
   |+++                                               | 5 % ~37s          
   |+++                                               | 6 % ~36s          
   |++++                                              | 7 % ~37s          
   |++++                                              | 8 % ~36s          
   |+++++                                             | 9 % ~35s          
   |++++++                                            | 10% ~34s          
   |++++++                                            | 11% ~34s          
   |+++++++                                           | 12% ~33s          
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   |++++++++                                          | 16% ~31s          
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   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 36s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~19s          
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   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 18s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~54s          
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Display the top markers you computed above.

tiss.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)
Error in filter_impl(.data, quo) : 
  Evaluation error: object 'avg_logFC' not found.

Generate an expression heatmap for given cells and genes. In this case, we are plotting the top 20 markers (or all markers if less than 20) for each cluster.

Assigning cell type identity to clusters

At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types: Cluster 0: basal cell of epidermis (CL:0002187) Cluster 1: differentiated keratinocytes (CL:0000312) Cluster 2: proliferating basal cell of epidermis (CL:0002187) Clutser 3: basal cell of epidermis (CL:0002187) Cluster 4: suprabasal differentiating keratinocytes (CL:0000312) Cluster 5: basal cell of epidermis (CL:0002187) Cluster 6: suprabasal differentiated keratinocytes (CL:0000312) Cluster 7: basal cell of epidermis (CL:0002187) Clutser 8: suprabasal differentiated keratinocytes (CL:0000312) Cluster 9: filiform differentiated keratinocytes (CL:0000312) Cluster 10: Langerhans cells (CL:0000453)

Checking for batch effects

Color by metadata, like plate barcode, to check for batch effects.

Print a table showing the count of cells in each identity category from each plate.

Save the Robject for later

When you save the annotated tissue, please give it a name.

Export the final metadata

So that Biohub can easily combine all your cell_ontology_classs, please export them as a simple csv.

---
 title: "Tongue Droplet Notebook"
 output: html_notebook
---

Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.

```{r}
tissue_of_interest = "Tongue"
library(here)
source(here("00_data_ingest", "02_tissue_analysis_rmd", "boilerplate.R"))
tiss = load_tissue_droplet(tissue_of_interest)
```


```{r, echo=FALSE, fig.height=16, fig.width=8}
#PCHeatmap(object = tiss, pc.use = 1:3, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 8)
PCHeatmap(object = tiss, pc.use = 1:10, cells.use = 500, do.balanced = TRUE, 
    label.columns = FALSE, use.full = FALSE)
```

Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.

```{r}
PCElbowPlot(object = tiss)
```

Choose the number of principal components to use.
```{r}
# Set number of principal components. 
n.pcs = 14
```


The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity--more connections within the group than between groups. The resolution parameter determines the scale...higher resolution will give more clusters, lower resolution will give fewer.

For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.

```{r}
# Set resolution 
res.used <- 0.4

tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
```


To visualize 
```{r}
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 20, perplexity=35, dim.embed = 2)
TSNEPlot(object = tiss, do.label = T, pt.size = 1.2, label.size = 4)
```


Check expression of genes of interset.

```{r, echo=FALSE, fig.height=16, fig.width=10}
genes_to_check = c('Itgb4', 'Itga6', 'Itgb1', 'Cdh3', 'Top2a', 'Mt2', 'Krt15', 'Krt5', 'Krt14', 'Sbsn', 'Krt10','Klf4'
, 'Krt84', 'Krt36', 'Hoxc13', 'Krt4', 'Krt13', 'Fabp5', 'Krt32',  'Krt78', 'Vim', 'Hhip', 'Ptch1', 'Gli1','Dsc1', 'Dcn', 'Runx2', 'Sox2', 'Pax9')
FeaturePlot(tiss, genes_to_check, pt.size = 1, nCol = 4)
```

Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.

```{r, echo=FALSE, fig.height=4, fig.width=20}
# To change the y-axis to show raw counts, add use.raw = T.
DotPlot(tiss, genes_to_check, plot.legend = T)
```


```{r, echo=FALSE, fig.height=20, fig.width=20}
# To change the y-axis to show raw counts, add use.raw = T.
VlnPlot(tiss, genes_to_check, y.log = TRUE)
```


How big are the clusters?
```{r}
table(tiss@ident)
```

```{r}
tiss1=BuildClusterTree(tiss)
```

```{r}
#cluster1.markers <- FindMarkers(object = tiss, ident.1 = 1, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster2.markers <- FindMarkers(object = tiss, ident.1 = 2, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster3.markers <- FindMarkers(object = tiss, ident.1 = 3, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster4.markers <- FindMarkers(object = tiss, ident.1 = 4, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster5.markers <- FindMarkers(object = tiss, ident.1 = 5, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
cluster6.markers <- FindMarkers(object = tiss, ident.1 = 6, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster7.markers <- FindMarkers(object = tiss, ident.1 = 7, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster8.markers <- FindMarkers(object = tiss, ident.1 = 8, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster9.markers <- FindMarkers(object = tiss, ident.1 = 9, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster0.markers <- FindMarkers(object = tiss, ident.1 = 0, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
```

Which markers identify a specific cluster?
```{r}
print(x = head(x = cluster6.markers, n = 30))
```


You can also compute all markers for all clusters at once. This may take some time.
```{r}
tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
```

Display the top markers you computed above.
```{r}
tiss.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)
```


Generate an expression heatmap for given cells and genes. In this case, we are plotting the top 20 markers (or all markers if less than 20) for each cluster.
```{r, echo=FALSE, fig.height=50, fig.width=35}
#top10 <- tiss.markers %>% group_by(cluster) %>% top_n(10, avg_logFC)
top20 <- tiss.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)
# setting slim.col.label to TRUE will print just the cluster IDS instead of
# every cell name
DoHeatmap(tiss, genes.use = top20$gene, slim.col.label = TRUE, remove.key = TRUE, cex.row = 20, group.cex = 20 )
```



## Assigning cell type identity to clusters

At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:
Cluster 0: basal cell of epidermis (CL:0002187)
Cluster 1: differentiated keratinocytes (CL:0000312)
Cluster 2: proliferating basal cell of epidermis (CL:0002187)
Clutser 3: basal cell of epidermis (CL:0002187) 
Cluster 4: suprabasal differentiating keratinocytes  (CL:0000312)
Cluster 5: basal cell of epidermis (CL:0002187) 
Cluster 6: suprabasal differentiated keratinocytes (CL:0000312)
Cluster 7: basal cell of epidermis (CL:0002187)
Clutser 8: suprabasal differentiated keratinocytes (CL:0000312)
Cluster 9: filiform differentiated keratinocytes (CL:0000312)
Cluster 10: Langerhans cells (CL:0000453)
```{r}
# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")

# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

free_annotation <- c(
 "basal_cells", 
 "differentiated", 
 "proliferating", 
 "basal_cells", 
 "suprabasal_differentiating", 
 "basal_cells", 
 "suprabasal_differentiated", 
 "basal_cells", 
 "suprabasal_differentiated", 
 "filiform_differentiated", 
 "Langerhans cells"
 )
cell_ontology_class <- c(
"basal cell of epidermis", 
"keratinocyte", 
"basal cell of epidermis",
"basal cell of epidermis",
"keratinocyte",
"basal cell of epidermis",
"keratinocyte",
"basal cell of epidermis",
"keratinocyte",
"keratinocyte",
 "Langerhans cells"
 )

validate_cell_ontology(cell_ontology_class)
cell_ontology_id = convert_to_cell_ontology_id(cell_ontology_class)

tiss@meta.data[,'free_annotation'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = free_annotation)
tiss@meta.data[,'cell_ontology_class'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_class)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)

tiss@meta.data[tiss@cell.names,'free_annotation'] <- as.character(tiss@meta.data$free_annotation)
tiss@meta.data[tiss@cell.names,'cell_ontology_class'] <- as.character(tiss@meta.data$cell_ontology_class)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)

TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='cell_ontology_class')
```


## Checking for batch effects


Color by metadata, like plate barcode, to check for batch effects.
```{r}
TSNEPlot(object = tiss, do.return = TRUE, group.by = "channel")
```

```{r}
TSNEPlot(object = tiss, do.return = TRUE, group.by = "mouse.sex")
```

Print a table showing the count of cells in each identity category from each plate.

```{r}
table(as.character(tiss@ident), as.character(tiss@meta.data$channel))
```

# Save the Robject for later
When you save the annotated tissue, please give it a name.

```{r}
filename = here('00_data_ingest', '04_tissue_robj_generated', 
		  paste0("droplet", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)
save(tiss, file=filename)
```

```{r}
# To reload a saved object
filename = here('00_data_ingest', '04_tissue_robj_generated', 
                      paste0("droplet", tissue_of_interest, "_seurat_tiss.Robj"))
 load(file=filename)
```


# Export the final metadata

So that Biohub can easily combine all your cell_ontology_classs, please export them as a simple csv.

```{r}
head(tiss@meta.data)
```


```{r}
filename = here('00_data_ingest', '03_tissue_annotation_csv', 
		  paste0(tissue_of_interest, "_droplet_annotation.csv"))
write.csv(FetchData(tiss, c('channel','cell_ontology_class','cell_ontology_id', 'free_annotation', 'tSNE_1', 'tSNE_2')), file=filename)
```
